1,578 research outputs found
Optimal Posted Prices for Online Cloud Resource Allocation
We study online resource allocation in a cloud computing platform, through a
posted pricing mechanism: The cloud provider publishes a unit price for each
resource type, which may vary over time; upon arrival at the cloud system, a
cloud user either takes the current prices, renting resources to execute its
job, or refuses the prices without running its job there. We design pricing
functions based on the current resource utilization ratios, in a wide array of
demand-supply relationships and resource occupation durations, and prove
worst-case competitive ratios of the pricing functions in terms of social
welfare. In the basic case of a single-type, non-recycled resource (i.e.,
allocated resources are not later released for reuse), we prove that our
pricing function design is optimal, in that any other pricing function can only
lead to a worse competitive ratio. Insights obtained from the basic cases are
then used to generalize the pricing functions to more realistic cloud systems
with multiple types of resources, where a job occupies allocated resources for
a number of time slots till completion, upon which time the resources are
returned back to the cloud resource pool
Dynamical phase transition in random walk subject to random drives
Random walk subject to random drive has been extensively employed as models
of physical and biological processes. While equilibrium statistical mechanics
has yielded significant insights into the distributions of dynamical fixed
points of such a system, its non-equilibrium properties are still largely left
unknown. In contrast, most real world applications concern dynamical aspects of
this model. In particular, dynamical quantities like heat dissipation and work
absorption plays a central role in predicting and controlling non-equilibrium
phases of matter. Recent advances in non-equilibrium statistical mechanics
enables a more refined study of the dynamical aspects of random walk under
random drives. We perform a numerical study on this model, and demonstrate that
this model exhibits two dynamical phases: a phase where typical random walk
trajectories are non-extensive and confined to neighborhood of fix points, and
another phase where typical random walk trajectories are extensive and can
transition between fixed points. We propose different summary statistics of the
heat dissipation, and show that these two phases are distinctively different.
Our characterization of these distinctive phases deepens the understanding of
and provides novel strategies for the nonequilibrium phase of this model.Comment: 7 pages, 8 figure
Draft Genome of the Leopard Gecko, \u3cem\u3eEublepharis Macularius\u3c/em\u3e
Background
Geckos are among the most species-rich reptile groups and the sister clade to all other lizards and snakes. Geckos possess a suite of distinctive characteristics, including adhesive digits, nocturnal activity, hard, calcareous eggshells, and a lack of eyelids. However, one gecko clade, the Eublepharidae, appears to be the exception to most of these ārulesā and lacks adhesive toe pads, has eyelids, and lays eggs with soft, leathery eggshells. These differences make eublepharids an important component of any investigation into the underlying genomic innovations contributing to the distinctive phenotypes in ātypicalā geckos. Findings
We report high-depth genome sequencing, assembly, and annotation for a male leopard gecko, Eublepharis macularius (Eublepharidae). Illumina sequence data were generated from seven insert libraries (ranging from 170 to 20 kb), representing a raw sequencing depth of 136X from 303 Gb of data, reduced to 84X and 187 Gb after filtering. The assembled genome of 2.02 Gb was close to the 2.23 Gb estimated by k-mer analysis. Scaffold and contig N50 sizes of 664 and 20 kb, respectively, were compble to the previously published Gekko japonicus genome. Repetitive elements accounted for 42 % of the genome. Gene annotation yielded 24,755 protein-coding genes, of which 93 % were functionally annotated. CEGMA and BUSCO assessment showed that our assembly captured 91 % (225 of 248) of the core eukaryotic genes, and 76 % of vertebrate universal single-copy orthologs. Conclusions
Assembly of the leopard gecko genome provides a valuable resource for future comptive genomic studies of geckos and other squamate reptiles
Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey
Data Augmentation (DA) has emerged as an indispensable strategy in Time
Series Classification (TSC), primarily due to its capacity to amplify training
samples, thereby bolstering model robustness, diversifying datasets, and
curtailing overfitting. However, the current landscape of DA in TSC is plagued
with fragmented literature reviews, nebulous methodological taxonomies,
inadequate evaluative measures, and a dearth of accessible, user-oriented
tools. In light of these challenges, this study embarks on an exhaustive
dissection of DA methodologies within the TSC realm. Our initial approach
involved an extensive literature review spanning a decade, revealing that
contemporary surveys scarcely capture the breadth of advancements in DA for
TSC, prompting us to meticulously analyze over 100 scholarly articles to
distill more than 60 unique DA techniques. This rigorous analysis precipitated
the formulation of a novel taxonomy, purpose-built for the intricacies of DA in
TSC, categorizing techniques into five principal echelons:
Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and
Automated Data Augmentation. Our taxonomy promises to serve as a robust
navigational aid for scholars, offering clarity and direction in method
selection. Addressing the conspicuous absence of holistic evaluations for
prevalent DA techniques, we executed an all-encompassing empirical assessment,
wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR
time-series datasets, employing ResNet and a multi-faceted evaluation paradigm
encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a
benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the
inconsistent efficacies of DA techniques, with..
- ā¦